Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging
Medical imaging is currently being applied in artificial intelligence and big data technologies in data formats. In order for medical imaging collected from different institutions and systems to be used for artificial intelligence data, interoperability is becoming a key element. Whilst interoperabi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/6/2704 |
| _version_ | 1797540947957907456 |
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| author | Oyun Kwon Sun K. Yoo |
| author_facet | Oyun Kwon Sun K. Yoo |
| author_sort | Oyun Kwon |
| collection | DOAJ |
| description | Medical imaging is currently being applied in artificial intelligence and big data technologies in data formats. In order for medical imaging collected from different institutions and systems to be used for artificial intelligence data, interoperability is becoming a key element. Whilst interoperability is currently guaranteed through medical data standards, compliance to personal information protection laws, and other methods, a standard solution for measurement values is deemed to be necessary in order for further applications as artificial intelligence data. As a result, this study proposes a model for interoperability in medical data standards, personal information protection methods, and medical imaging measurements. This model applies Health Level Seven (HL7) and Digital Imaging and Communications in Medicine (DICOM) standards to medical imaging data standards and enables increased accessibility towards medical imaging data in the compliance of personal information protection laws through the use of de-identifying methods. This study focuses on offering a standard for the measurement values of standard materials that addresses uncertainty in measurements that pre-existing medical imaging measurement standards did not provide. The study finds that medical imaging data standards conform to pre-existing standards and also provide protection to personal information within any medical images through de-identifying methods. Moreover, it proposes a reference model that increases interoperability by composing a process that minimizes uncertainty using standard materials. The interoperability reference model is expected to assist artificial intelligence systems using medical imaging and further enhance the resilience of future health technologies and system development. |
| first_indexed | 2024-03-10T13:08:20Z |
| format | Article |
| id | doaj.art-061ec395c4844c668760f058e6872b0c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T13:08:20Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-061ec395c4844c668760f058e6872b0c2023-11-21T10:54:51ZengMDPI AGApplied Sciences2076-34172021-03-01116270410.3390/app11062704Interoperability Reference Models for Applications of Artificial Intelligence in Medical ImagingOyun Kwon0Sun K. Yoo1Department of Medical Engineering, Yonsei University College of Medicine, Seoul 03722, KoreaDepartment of Medical Engineering, Yonsei University College of Medicine, Seoul 03722, KoreaMedical imaging is currently being applied in artificial intelligence and big data technologies in data formats. In order for medical imaging collected from different institutions and systems to be used for artificial intelligence data, interoperability is becoming a key element. Whilst interoperability is currently guaranteed through medical data standards, compliance to personal information protection laws, and other methods, a standard solution for measurement values is deemed to be necessary in order for further applications as artificial intelligence data. As a result, this study proposes a model for interoperability in medical data standards, personal information protection methods, and medical imaging measurements. This model applies Health Level Seven (HL7) and Digital Imaging and Communications in Medicine (DICOM) standards to medical imaging data standards and enables increased accessibility towards medical imaging data in the compliance of personal information protection laws through the use of de-identifying methods. This study focuses on offering a standard for the measurement values of standard materials that addresses uncertainty in measurements that pre-existing medical imaging measurement standards did not provide. The study finds that medical imaging data standards conform to pre-existing standards and also provide protection to personal information within any medical images through de-identifying methods. Moreover, it proposes a reference model that increases interoperability by composing a process that minimizes uncertainty using standard materials. The interoperability reference model is expected to assist artificial intelligence systems using medical imaging and further enhance the resilience of future health technologies and system development.https://www.mdpi.com/2076-3417/11/6/2704interoperabilityde-identifiersmeasurement uncertaintystandardization |
| spellingShingle | Oyun Kwon Sun K. Yoo Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging Applied Sciences interoperability de-identifiers measurement uncertainty standardization |
| title | Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging |
| title_full | Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging |
| title_fullStr | Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging |
| title_full_unstemmed | Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging |
| title_short | Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging |
| title_sort | interoperability reference models for applications of artificial intelligence in medical imaging |
| topic | interoperability de-identifiers measurement uncertainty standardization |
| url | https://www.mdpi.com/2076-3417/11/6/2704 |
| work_keys_str_mv | AT oyunkwon interoperabilityreferencemodelsforapplicationsofartificialintelligenceinmedicalimaging AT sunkyoo interoperabilityreferencemodelsforapplicationsofartificialintelligenceinmedicalimaging |